Predictive Data Analysis-Based Modeling of Electric Vehicle Future Driving Range 


Vol. 15,  No. 1, pp. 21-27, Jan.  2026
10.3745/TKIPS.2026.15.1.21


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  Abstract

As interest in eco-friendly transportation methods increases, battery degradation in Electric Vehicles (EVs) poses a critical barrier to the adoption of EVs. Our research aims to predict the range of EVs one year into the future and use this prediction as an indicator to diagnose the degree of battery degradation. Battery Management System data collected from 371 vehicles over two years underwent preprocessing and feature engineering before being used for Feature Tokenizer Transformer model training. The Feature Tokenizer Transformer has structural advantages that enabling effective capture of complex relationships among all input variables regardless of variable types, which makes it well-suited for processing tabular data such as Battery Management System data. In actual EV operation, most cases involve charging before the battery is completely discharged, which makes it difficult to collect ground truth values of range required for model training. To overcome this constraint, we propose a method that predicts energy efficiency one year ahead and subsequently converts it to range. Through a stepwise prediction framework divided into energy efficiency prediction and range calculation, we can predict range without complete discharge data from EV batteries. This research achieved a prediction accuracy of Mean Absolute Error 8.872(km), which confirms that our methodology can be effectively applied to future range prediction problems based on real-world collected data.

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[IEEE Style]

H. Jeong, D. Lee, K. Cho, C. Hong, "Predictive Data Analysis-Based Modeling of Electric Vehicle Future Driving Range," The Transactions of the Korea Information Processing Society, vol. 15, no. 1, pp. 21-27, 2026. DOI: 10.3745/TKIPS.2026.15.1.21.

[ACM Style]

Heeseo Jeong, Dajeong Lee, Kyujin Cho, and Charmgil Hong. 2026. Predictive Data Analysis-Based Modeling of Electric Vehicle Future Driving Range. The Transactions of the Korea Information Processing Society, 15, 1, (2026), 21-27. DOI: 10.3745/TKIPS.2026.15.1.21.

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